The Interplay Between Human Leukocyte Antigen Antibody Profile and COVID-19 Vaccination in Waitlisted Renal Transplant Patients
Bibliographic record
Abstract
CONTEXT.—: Mass COVID-19 vaccination is mandated in vulnerable populations in our renal transplant waitlist cohort. However, the anti-human leukocyte antigen (anti-HLA) profile after COVID-19 vaccination is controversial, and the side effects are yet to be discerned. OBJECTIVE.—: To evaluate the status of HLA antibodies in waitlisted renal transplant patients before and 3 weeks after each vaccination and if comorbidities are associated with the HLA antibody profile. DESIGN.—: A total of 59 waitlisted kidney transplant patients were included in this study. The anti-HLA antibodies were analyzed before and 6 months after their last COVID-19 vaccination. The mean fluorescence intensity change in the anti-HLA antibody levels was used to classify patients into 3 groups: high inducers, low inducers, and noninducers. RESULTS.—: There were significant HLA antibody profile changes after COVID-19 vaccination, showing 21 antibodies generated against HLA class I antigens and 7 against HLA class II antigens to their baseline. Compared with the noninducers, the high and low inducers showed a higher prevalence of COVID-19 infection, COVID-19 vaccine type, and background hypertension history. CONCLUSIONS.—: Our data suggest that COVID-19 vaccination propagates anti-HLA class I and II antibodies for waitlisted renal transplant patients. The clinical significance of these antibodies needs further study. Furthermore, comorbidities, such as history of COVID-19 infection and hypertension, supplemented this effect. Anti-HLA antibody monitoring may be warranted in COVID-19 vaccinated, waitlisted renal transplant patients with a history of COVID-19 infection and/or hypertension.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".